#define RELU(x) fmax(x, 0.0f)

__constant sampler_t samplerN = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;

__constant float kernelsL1[9 * 8] = 
{
-0.0461f,  0.1274f,  0.2976f,
-0.0393f, -0.1251f,  0.2527f,
 0.0791f,  0.0600f, -0.0303f,
-0.0520f, -0.5039f, -0.3305f,
-0.0115f,  0.0456f,  0.4370f,
 0.0601f,  0.0780f,  0.3106f,
-0.0017f, -0.0018f, -0.0017f,
-0.0017f, -0.0018f, -0.0018f,
-0.0017f, -0.0017f, -0.0017f,
 0.2666f,  0.1687f,  0.2303f,
-0.1901f,  0.3825f,  0.3024f,
 0.1811f,  0.0581f,  0.2080f,
-0.1246f,  0.0155f, -0.4075f,
 0.1156f,  0.5929f,  0.1449f,
-0.1080f, -0.0171f, -0.0516f,
-0.0817f,  0.2247f,  0.0472f,
 0.0394f,  0.1085f,  0.1435f,
-0.0480f, -0.0135f, -0.0606f,
-0.0083f,  0.2045f,  0.1056f,
-0.2239f,  0.2823f, -0.1926f,
 0.2581f,  0.1362f, -0.1914f,
-0.0833f,  0.0702f,  0.0234f,
 0.3616f,  0.3789f, -0.1840f,
 0.0128f,  0.1347f, -0.0187f
};

__constant float biasL1[8] = 
{
-0.1329f, -0.0431f, -0.0031f, -0.0129f,  0.2294f, -0.2595f, -0.2370f, -0.0499f
};

__constant float kernelsL[8][9 * 8 * 8] = 
{
{
 1.4090e-01f, -1.8985e-02f, -6.8589e-02f,
 6.6491e-02f,  1.4360e-02f,  8.5223e-02f,
 1.8782e-01f,  9.8042e-02f, -3.4558e-02f,
 2.5606e-01f,  2.2027e-01f,  2.7603e-01f,
 1.9424e-01f,  3.4537e-02f,  9.5975e-02f,
 1.1223e-02f, -4.3377e-01f, -1.4760e-01f,
-3.4293e-40f, -5.5421e-40f, -4.4763e-41f,
-6.3322e-40f, -3.1495e-40f, -7.8264e-41f,
-1.5375e-40f, -3.3656e-40f,  5.2441e-40f,
 1.2413e-01f,  1.5682e-01f,  1.1465e-01f,
 1.6683e-02f,  7.8382e-02f,  1.0110e-01f,
 1.4902e-01f,  1.3608e-01f,  1.1674e-01f,
-6.5160e-02f,  7.7748e-02f,  2.1773e-02f,
 2.0652e-02f,  2.7245e-01f,  1.0297e-01f,
-2.0953e-02f,  6.1685e-02f,  4.4128e-02f,
 6.1538e-02f, -1.9746e-02f, -1.2785e-02f,
 2.5931e-02f,  1.2740e-01f,  9.0033e-02f,
 8.6448e-02f,  2.0684e-01f,  9.8063e-02f,
-7.8384e-03f,  6.3277e-02f,  7.6751e-03f,
 3.5956e-02f,  1.0555e-01f,  4.2728e-02f,
 7.1578e-02f,  1.3253e-01f,  1.1171e-01f,
-2.7538e-02f,  1.5836e-01f,  1.0014e-01f,
-4.9113e-02f,  1.6911e-01f,  2.7329e-01f,
 7.9170e-03f,  9.5440e-02f,  1.3922e-01f,
 8.0151e-02f,  4.3438e-02f,  5.5314e-02f,
 3.4896e-02f,  1.6816e-01f, -4.5783e-03f,
-1.4579e-03f,  2.0493e-01f,  2.6238e-02f,
 2.6499e-02f,  3.9490e-01f, -1.1582e-02f,
 3.5790e-01f,  1.4317e-01f, -2.1775e-01f,
 4.1794e-03f, -3.2513e-01f, -1.6729e-01f,
 3.4040e-41f, -6.2960e-42f, -1.0067e-40f,
 5.5978e-41f, -1.2353e-40f, -1.1347e-40f,
 5.4572e-40f, -6.4384e-40f, -4.1234e-40f,
-9.3690e-02f,  1.7765e-01f,  1.1275e-01f,
 9.1159e-03f,  1.7375e-01f,  1.1427e-01f,
-7.8385e-02f,  1.5658e-01f, -3.8399e-02f,
-1.0756e-01f,  5.9943e-02f, -6.7273e-02f,
-1.1117e-01f,  1.5267e-01f,  1.1563e-01f,
-1.2964e-01f, -3.8604e-02f, -2.4532e-02f,
 1.6324e-02f,  1.3112e-01f,  6.1679e-03f,
-7.7703e-03f,  2.6311e-01f,  8.9427e-02f,
-2.8948e-02f,  1.9341e-01f,  4.4339e-02f,
 6.4559e-03f, -6.8885e-02f,  1.1481e-01f,
-1.0665e-01f,  3.8613e-02f,  7.0410e-02f,
-6.1680e-02f, -1.7374e-02f,  9.5475e-03f,
-4.0081e-02f, -3.1549e-02f,  2.8311e-01f,
-1.2178e-01f, -1.3848e-01f,  1.7416e-01f,
-8.1756e-02f, -1.7718e-01f,  7.9533e-02f,
-3.1299e-03f, -3.2305e-03f, -3.2094e-03f,
-3.1548e-03f, -3.2553e-03f, -3.2453e-03f,
-3.1459e-03f, -3.2278e-03f, -3.2076e-03f,
-3.6554e-05f, -3.6715e-05f, -3.1284e-05f,
-1.4927e-05f, -1.4357e-05f, -1.2185e-05f,
-1.5771e-09f, -1.1439e-09f, -6.4952e-10f,
 3.7723e-40f,  4.9166e-40f, -2.1946e-40f,
-4.7599e-40f, -4.3356e-40f, -8.3928e-41f,
 2.6127e-40f,  4.8634e-40f,  2.7720e-40f,
-5.4972e-03f, -5.6409e-03f, -5.6919e-03f,
-5.5818e-03f, -5.7079e-03f, -5.7542e-03f,
-5.6338e-03f, -5.7437e-03f, -5.7600e-03f,
-3.7940e-03f, -3.8853e-03f, -3.8693e-03f,
-3.8995e-03f, -3.9616e-03f, -3.8945e-03f,
-3.8438e-03f, -3.9156e-03f, -3.8269e-03f,
-7.2342e-05f, -7.8682e-05f, -4.7701e-05f,
-1.1126e-04f, -1.1918e-04f, -7.8931e-05f,
-1.1644e-04f, -1.2418e-04f, -8.2350e-05f,
-2.3881e-04f, -3.7971e-04f, -3.9448e-04f,
-2.4112e-04f, -3.8395e-04f, -4.0189e-04f,
-2.3451e-04f, -3.7525e-04f, -3.9222e-04f,
-3.9853e-03f, -4.0748e-03f, -4.1134e-03f,
-4.0685e-03f, -4.1456e-03f, -4.1548e-03f,
-4.0547e-03f, -4.1388e-03f, -4.1357e-03f,
 5.3008e-02f,  2.2252e-02f, -7.1158e-02f,
-6.6411e-02f, -3.0015e-02f, -2.2526e-02f,
 1.2259e-01f, -6.2488e-02f,  5.6190e-02f,
 1.5981e-02f, -7.6832e-02f,  1.7908e-02f,
 2.7618e-01f,  5.4054e-02f,  8.7282e-02f,
 1.5212e-02f, -1.1097e-01f, -2.2265e-02f,
-6.8532e-41f, -6.0539e-40f,  4.6269e-40f,
-2.9221e-40f, -3.8468e-40f, -4.6656e-40f,
 6.4572e-40f, -6.1625e-40f,  6.4545e-40f,
 3.5920e-02f,  9.0955e-02f, -1.7626e-02f,
 4.7826e-02f,  1.8832e-01f, -4.4043e-02f,
-3.8405e-02f,  5.9176e-02f,  6.8182e-02f,
 3.7657e-03f,  2.6441e-02f, -2.5585e-01f,
 1.0969e-01f,  2.3914e-01f,  3.5120e-02f,
-1.6252e-01f,  3.4371e-02f, -2.7501e-01f,
 4.9289e-02f,  2.2088e-02f, -1.4588e-02f,
 1.6384e-01f, -8.1421e-03f, -6.9613e-02f,
 1.0820e-01f,  1.1137e-01f,  7.2648e-03f,
 1.5243e-01f,  1.3659e-01f,  2.7553e-02f,
 1.3966e-01f,  1.1019e-01f,  1.9817e-02f,
 1.1420e-01f, -5.1386e-03f,  6.8617e-03f,
-1.3264e-02f,  2.1508e-01f,  4.8430e-02f,
 5.1149e-02f,  2.9165e-01f,  2.8077e-01f,
 2.9288e-03f,  9.0611e-02f,  8.1538e-02f,
-1.1812e-01f,  1.5603e-02f,  1.1571e-01f,
-3.4958e-02f, -1.6688e-03f, -4.6619e-02f,
-1.0417e-02f, -3.1802e-02f,  1.8357e-02f,
 1.1064e-01f,  1.8397e-01f,  4.8449e-02f,
-8.3336e-03f,  1.6029e-01f,  3.9490e-02f,
-4.0959e-01f, -2.6134e-01f,  2.0766e-02f,
 6.6073e-41f, -6.7490e-40f, -5.1131e-41f,
-4.3320e-41f, -3.7194e-40f,  2.0674e-40f,
-5.2359e-40f, -3.4006e-40f, -4.9257e-40f,
-4.7260e-02f,  2.8518e-03f, -2.7764e-01f,
 6.9182e-03f,  1.3938e-01f, -1.3162e-01f,
-6.0901e-03f,  1.0339e-01f,  6.0419e-02f,
-1.4449e-01f, -3.2043e-02f, -9.1466e-02f,
-1.4022e-02f,  3.1703e-01f,  5.8166e-02f,
-1.5243e-02f,  1.4521e-01f,  2.0790e-04f,
-1.0255e-01f, -7.8766e-02f, -1.2395e-01f,
 7.9894e-03f,  3.7079e-03f, -3.2134e-02f,
 1.1663e-01f,  1.4808e-01f,  2.0431e-01f,
 7.4026e-02f,  6.9632e-02f,  1.7156e-01f,
-3.0385e-02f,  2.3218e-01f,  7.3855e-02f,
-8.8530e-02f, -5.9224e-02f,  2.3431e-02f,
 1.4596e-02f,  3.2442e-02f, -1.1308e-01f,
-6.3734e-02f,  2.5270e-01f,  7.8081e-02f,
 1.0468e-02f,  1.5473e-01f,  3.8676e-02f,
-1.0842e-01f,  8.6778e-03f,  1.4985e-01f,
 8.1757e-03f, -8.2109e-02f,  8.5471e-02f,
-2.1437e-01f, -6.1173e-02f,  4.8163e-02f,
 2.8965e-01f,  1.9748e-01f,  4.2651e-02f,
 1.8196e-01f,  3.3932e-01f,  3.9594e-01f,
 3.9657e-01f,  4.2167e-01f,  2.9290e-01f,
 7.4011e-41f,  6.5220e-40f, -5.9885e-40f,
 7.4011e-41f,  6.2047e-40f, -7.1533e-40f,
 4.1950e-40f, -1.1886e-40f, -5.9922e-40f,
 1.9662e-01f,  2.1402e-01f,  3.1041e-02f,
-1.1079e-01f,  1.3361e-01f, -2.1608e-01f,
-1.7962e-01f, -8.0576e-02f, -3.1277e-01f,
 1.0620e-02f,  2.4024e-01f,  1.0657e-01f,
-7.9906e-05f,  2.8760e-01f,  4.1231e-02f,
-1.3261e-02f, -1.0868e-01f, -1.1267e-01f,
-1.0659e-02f, -2.6051e-02f, -4.5389e-02f,
 5.8261e-02f,  4.0288e-02f,  6.7050e-02f,
-2.6462e-01f, -1.7846e-01f, -1.0002e-01f,
-6.2904e-02f,  1.5275e-01f,  4.4282e-03f,
 1.4446e-01f,  1.1814e-01f, -8.0349e-02f,
 2.0331e-02f,  3.3014e-02f,  1.2710e-01f,
 1.6084e-01f,  3.8819e-01f,  1.0854e-01f,
-6.8126e-03f,  3.5673e-01f,  1.8938e-01f,
-1.1660e-01f, -5.7694e-02f, -2.9194e-01f,
 1.2775e-02f, -3.2769e-02f,  1.7228e-02f,
 1.8324e-01f,  1.1983e-01f, -1.6944e-02f,
 1.0593e-01f,  1.3451e-01f,  5.2536e-02f,
 1.9147e-01f,  1.3875e-01f,  1.0298e-01f,
-2.0871e-01f, -1.7197e-01f,  1.1342e-01f,
-1.7581e-01f,  4.0972e-02f,  2.9796e-01f,
 3.2588e-40f, -4.3663e-40f, -2.6518e-40f,
 3.2588e-40f, -4.3663e-40f, -2.6518e-40f,
 4.1600e-40f, -4.4350e-40f, -4.8744e-41f,
 3.7289e-02f,  8.1769e-03f,  1.7059e-02f,
 3.7735e-02f,  6.6571e-02f, -6.6137e-02f,
-5.8890e-02f, -7.7019e-03f, -6.2128e-02f,
-4.0751e-02f,  1.1710e-01f, -1.1586e-01f,
-1.2999e-01f, -1.6384e-02f, -2.1858e-01f,
-2.8028e-01f, -6.0443e-02f, -1.1880e-01f,
 1.8152e-01f,  1.5364e-01f,  1.1781e-01f,
 2.9010e-01f,  2.4612e-01f,  1.3170e-01f,
 1.9022e-01f,  1.8117e-01f,  1.6483e-01f,
 9.3342e-02f,  2.6607e-01f,  1.4679e-01f,
 1.6729e-01f,  2.5374e-01f,  1.1954e-01f,
 6.3258e-02f,  1.0557e-01f,  6.7221e-02f,
-5.2017e-02f,  1.9628e-01f,  1.7243e-01f,
-3.2667e-02f,  1.5756e-01f,  1.9347e-01f,
-9.5252e-02f, -3.7525e-02f, -3.4543e-04f,
-4.9759e-02f,  4.0383e-02f, -2.0231e-02f,
-1.1776e-01f,  3.4182e-02f,  3.6720e-02f,
-1.4822e-02f, -4.1658e-02f, -1.3729e-02f,
-1.9215e-02f,  2.4427e-02f, -9.0638e-02f,
-1.4438e-01f, -2.1785e-01f, -5.1789e-02f,
-2.0279e-01f, -3.3918e-01f, -1.6871e-01f,
 6.1262e-41f,  2.4066e-40f,  6.6851e-40f,
 5.3430e-40f, -3.2335e-40f, -3.7400e-40f,
-6.3256e-40f, -4.7491e-40f,  2.2854e-40f,
-6.8701e-03f, -1.4849e-02f,  8.6332e-02f,
 1.1686e-01f,  1.8346e-01f,  1.8797e-01f,
-2.3251e-02f,  7.3973e-02f,  1.0532e-01f,
-6.1838e-02f,  5.6667e-02f,  8.1584e-02f,
-3.8900e-02f,  7.0927e-02f,  9.5606e-02f,
-4.5098e-02f, -1.0829e-01f, -1.2224e-01f,
 3.5047e-03f,  3.2898e-02f,  3.5622e-02f,
 1.6170e-02f,  4.3721e-02f,  9.7496e-02f,
 2.3445e-03f,  6.0417e-02f,  1.3482e-01f,
 6.0570e-02f, -5.7139e-03f, -1.0883e-03f,
 2.2701e-02f, -2.9113e-02f,  7.9178e-03f,
 8.1214e-02f, -4.1408e-02f,  1.3616e-02f,
-4.7985e-02f,  1.0304e-02f, -3.3236e-02f,
-1.6334e-02f, -8.1538e-02f,  1.8629e-02f,
-9.3720e-02f, -1.2920e-01f, -4.0836e-02f
}
,
{
 1.0443e-01f,  1.5461e-01f, -1.4743e-01f,
 1.6716e-01f,  1.0532e-01f, -2.3088e-01f,
 1.0218e-01f,  1.2393e-01f, -9.6646e-02f,
 1.7659e-01f, -7.3279e-02f,  1.9627e-02f,
 1.7721e-01f, -1.4329e-01f, -1.2533e-01f,
 1.6551e-01f, -3.4616e-01f,  9.5618e-02f,
 4.5827e-09f,  9.3413e-09f,  1.7015e-08f,
 1.2245e-08f,  9.9727e-09f,  6.7108e-09f,
 1.9612e-07f,  3.9479e-08f,  1.1537e-09f,
 2.2127e-02f,  9.2715e-02f, -1.2150e-01f,
 7.5652e-02f,  1.1548e-01f, -1.2420e-01f,
-1.0693e-03f, -7.2839e-02f, -1.9664e-01f,
 1.4466e-01f, -1.8552e-03f, -1.3575e-01f,
 2.0699e-01f,  8.0396e-02f, -1.9651e-01f,
-4.7075e-02f, -5.1259e-02f, -8.2593e-02f,
-2.2385e-01f,  3.0066e-03f, -2.2659e-02f,
 6.1827e-02f,  2.5331e-02f, -5.3898e-02f,
 2.7091e-01f,  1.0991e-01f, -3.3600e-01f,
-8.9499e-02f, -9.3821e-03f,  2.2675e-02f,
 1.1213e-01f,  1.3276e-01f,  2.0368e-02f,
 6.5408e-02f,  4.1598e-02f, -4.7917e-02f,
 6.0740e-03f,  1.2236e-04f, -1.0659e-01f,
-1.8072e-02f, -9.1082e-02f, -9.0414e-02f,
 4.9052e-02f, -1.4298e-01f, -3.9721e-02f,
 1.1840e-01f,  2.2503e-01f,  2.4587e-02f,
 9.3023e-02f,  6.9650e-02f,  1.6798e-01f,
-1.5640e-03f,  1.6300e-02f,  6.3585e-02f,
 1.4431e-01f,  3.7885e-02f,  1.6692e-02f,
 1.7345e-01f,  7.2315e-02f,  1.8942e-02f,
 1.1081e-01f,  8.2973e-02f, -9.7717e-02f,
-5.2264e-03f, -5.2641e-03f, -5.2727e-03f,
-5.2809e-03f, -5.3125e-03f, -5.3153e-03f,
-5.2915e-03f, -5.3251e-03f, -5.3231e-03f,
 6.0008e-02f,  2.0268e-01f,  1.3396e-01f,
-2.5202e-03f, -1.7750e-02f, -1.2019e-02f,
 1.1806e-01f, -2.2306e-02f,  3.6464e-02f,
 7.9324e-02f,  3.1883e-02f,  1.5483e-02f,
-4.3537e-02f,  1.2204e-02f,  1.8905e-02f,
-8.1581e-02f, -1.1307e-01f, -6.0718e-02f,
-2.4865e-01f, -1.0199e-01f,  1.9886e-02f,
-1.0519e-02f,  6.9972e-02f,  4.8012e-02f,
-1.5282e-02f,  1.1979e-01f,  8.7968e-02f,
-3.6752e-02f,  1.9523e-02f,  7.1321e-02f,
-5.8295e-02f,  5.3242e-02f,  1.2773e-01f,
-7.9671e-02f,  8.3249e-04f,  7.4904e-02f,
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 6.5257e-02f,  1.4804e-01f, -6.2092e-02f,
-1.5699e-02f, -1.5303e-02f,  1.6671e-01f,
-6.1947e-03f,  2.5749e-01f,  1.5257e-01f,
 3.2908e-02f, -5.9907e-02f,  1.1502e-01f,
 7.5876e-02f, -2.6699e-01f, -1.5891e-02f,
-8.0426e-02f,  1.3406e-01f, -1.9881e-02f,
 3.5472e-02f, -8.2140e-02f,  1.6509e-02f,
 8.3390e-03f, -7.8291e-02f, -2.0754e-01f,
 3.4490e-02f,  2.7913e-01f,  5.9566e-02f,
 2.5288e-02f,  1.1725e-01f, -1.0356e-01f,
-5.0955e-02f,  9.2093e-02f, -5.8477e-02f,
 4.4325e-02f,  3.2973e-02f, -1.9477e-01f,
 3.9582e-02f, -8.6877e-02f, -1.1753e-01f,
 3.0401e-02f, -2.8757e-02f, -2.5563e-02f,
 5.0741e-02f, -3.5056e-01f, -2.5584e-01f,
 9.1709e-02f, -4.0932e-02f,  2.3812e-01f,
 5.0945e-02f,  4.9246e-02f,  1.2738e-01f,
 5.1440e-03f,  1.5703e-01f,  5.5743e-02f,
-3.9492e-02f,  1.2114e-01f,  2.0531e-02f,
 8.0800e-02f,  2.6680e-03f, -1.6660e-02f,
 1.0684e-01f,  1.2308e-01f,  1.7882e-02f,
 1.8280e-02f,  1.0972e-01f, -5.2912e-03f
}
,
{
-1.3812e-02f, -4.6271e-02f,  7.3790e-02f,
-6.3801e-02f, -3.6817e-01f, -1.7880e-02f,
 5.2986e-02f,  1.8626e-01f,  1.5645e-03f,
 1.2367e-02f, -6.2923e-02f,  3.0844e-02f,
 9.3623e-02f,  1.9527e-01f, -2.6366e-02f,
-2.0837e-02f, -3.4424e-02f,  4.0256e-02f,
 4.1482e-02f,  6.1795e-02f, -1.1293e-02f,
-8.9944e-02f, -1.3608e-01f,  1.8067e-02f,
 3.6974e-02f,  5.2530e-03f, -2.7474e-02f,
 1.1872e-05f,  1.9000e-05f,  2.0729e-05f,
 1.0139e-05f,  1.6832e-05f,  1.9392e-05f,
 6.5445e-06f,  1.0973e-05f,  1.3521e-05f,
-5.3340e-02f,  1.3108e-03f,  4.0436e-02f,
 5.7068e-02f, -2.7923e-02f, -5.4781e-02f,
-2.9293e-02f,  2.7145e-02f,  2.7340e-02f,
 5.3520e-03f,  1.8766e-02f,  4.0297e-01f,
 2.6473e-02f, -3.4675e-02f, -1.1783e-01f,
-2.5038e-02f, -1.7702e-02f, -3.4908e-02f,
 1.4847e-02f,  2.3237e-01f, -6.3687e-02f,
-6.5672e-02f, -2.1888e-01f, -1.7233e-02f,
 4.0608e-02f, -6.9580e-02f, -2.2200e-02f,
 5.8163e-02f,  1.3695e-01f, -2.6257e-02f,
-1.3328e-01f, -3.5730e-01f,  2.4507e-02f,
-4.5611e-03f,  2.0424e-01f, -3.9821e-02f,
 5.5300e-02f, -1.6006e-01f,  1.1717e-01f,
-2.6107e-02f, -8.6995e-02f,  8.3720e-02f,
 7.5494e-02f,  3.2189e-01f,  1.5527e-01f,
-6.6869e-02f,  1.4469e-01f,  5.1805e-02f,
 9.8760e-02f, -1.6759e-01f, -1.2350e-01f,
 5.7005e-02f,  8.4904e-02f,  8.9713e-02f,
-1.4263e-02f,  2.8914e-02f,  3.2239e-02f,
-2.4871e-02f,  5.6014e-02f, -4.4469e-02f,
 3.1209e-02f,  1.3677e-02f, -2.1052e-02f,
-1.6548e-03f, -1.8796e-03f, -1.9883e-03f,
-1.6186e-03f, -1.8494e-03f, -1.9670e-03f,
-1.5841e-03f, -1.8173e-03f, -1.9345e-03f,
 3.5726e-02f,  1.8013e-01f,  1.6913e-02f,
-1.2168e-01f, -6.3848e-02f,  3.0555e-02f,
 3.0269e-02f, -1.0260e-01f, -1.5259e-02f,
-4.7375e-03f,  5.5115e-02f,  6.2642e-01f,
 9.9776e-03f, -2.1988e-01f, -2.0984e-01f,
 7.0470e-03f,  6.3178e-02f, -1.3607e-02f,
 1.1918e-01f, -2.4081e-01f,  1.7889e-01f,
-1.0514e-01f,  2.9220e-01f, -1.3263e-01f,
 5.6091e-03f, -4.1623e-02f,  2.5589e-02f,
-1.8496e-01f,  2.7698e-02f, -6.5768e-02f,
 2.9677e-01f,  4.4163e-02f,  5.8530e-02f,
-1.1010e-01f, -7.6787e-02f,  3.9844e-02f,
 5.2113e-03f, -1.8202e-02f,  1.4129e-03f,
-6.1402e-03f, -2.7222e-01f,  7.4690e-02f,
 1.9131e-02f,  2.2753e-01f,  1.9587e-02f,
-2.7391e-02f,  6.7917e-03f,  2.0496e-03f,
 6.7333e-02f,  7.8262e-02f,  2.1110e-03f,
-5.4519e-02f,  3.0763e-02f,  1.5628e-02f,
 9.5055e-02f,  3.8855e-02f,  1.2446e-02f,
-1.5152e-01f,  7.8124e-02f, -1.2616e-02f,
 9.3100e-03f, -1.6528e-02f, -1.2873e-02f,
-1.8377e-03f, -1.9231e-03f, -1.8930e-03f,
-1.8058e-03f, -1.8841e-03f, -1.8678e-03f,
-1.7387e-03f, -1.7966e-03f, -1.7781e-03f,
-4.5122e-02f,  1.7027e-03f, -3.5534e-03f,
 8.5222e-03f,  1.0130e-01f,  4.7893e-02f,
 6.5574e-02f,  7.2150e-03f, -2.1820e-03f,
-5.5105e-03f, -1.8990e-01f,  2.6527e-02f,
 6.6140e-03f,  2.1537e-01f, -2.2183e-02f,
-8.0628e-03f,  6.8398e-03f,  9.4474e-03f,
 1.2239e-01f, -1.3337e-01f,  7.3391e-02f,
-1.2205e-01f,  1.3145e-01f, -2.0063e-02f,
 2.2168e-02f,  3.6097e-03f,  2.7146e-02f,
 4.6717e-02f,  2.1122e-02f,  1.5491e-02f,
-1.3077e-01f,  1.1635e-01f,  1.0849e-02f,
 8.0113e-02f, -8.4028e-02f,  1.2863e-03f,
-2.9796e-02f, -8.4537e-02f, -2.6766e-03f,
-7.7771e-03f, -2.4274e-03f,  8.6274e-02f,
-2.0354e-02f,  4.1245e-02f,  8.4227e-02f,
 5.5894e-02f,  1.0706e-01f,  5.2965e-02f,
-7.8731e-03f,  5.5825e-01f,  1.0373e-01f,
-1.1975e-01f, -2.0071e-02f, -2.5286e-02f,
-7.7477e-02f,  5.3589e-02f, -1.5710e-03f,
-1.2753e-01f,  2.5166e-01f,  8.2205e-03f,
-9.8349e-02f, -4.9539e-02f, -5.4941e-02f,
-4.9916e-03f, -4.9986e-03f, -5.0660e-03f,
-4.9770e-03f, -4.9840e-03f, -5.0543e-03f,
-4.9997e-03f, -5.0114e-03f, -5.0809e-03f,
 6.1819e-02f,  1.5061e-01f,  1.1984e-02f,
 1.2905e-01f,  2.5921e-01f,  1.4768e-01f,
 4.5548e-02f,  1.4902e-01f, -4.8961e-03f,
-1.3605e-02f,  8.2896e-02f, -4.1931e-01f,
-2.2657e-02f,  2.4768e-01f,  2.6528e-01f,
-1.1566e-02f, -8.7819e-03f,  4.3618e-02f,
-3.4332e-02f, -1.8392e-01f,  4.4471e-02f,
-3.7073e-02f, -5.4620e-02f,  1.0899e-01f,
 3.7891e-02f,  9.9487e-02f,  3.2383e-02f,
-6.3628e-02f, -5.0303e-03f,  5.4617e-02f,
-8.7802e-02f,  2.1977e-01f, -6.0249e-03f,
 6.3554e-02f, -5.4291e-02f, -2.6709e-02f,
-1.5505e-02f, -6.7104e-02f,  3.8607e-02f,
-1.1427e-01f, -3.2524e-01f,  4.0077e-02f,
-6.5144e-03f,  1.2313e-01f, -2.7924e-02f,
 1.4265e-02f, -3.8338e-02f,  8.6780e-02f,
 1.5341e-01f,  1.2174e-01f, -7.3160e-02f,
 2.6326e-04f,  7.3690e-02f,  5.2187e-02f,
-3.3114e-02f, -3.6588e-02f,  1.1635e-02f,
-3.3521e-02f,  1.0767e-01f, -8.9125e-03f,
-2.2431e-02f, -4.5655e-03f,  7.5531e-03f,
 6.7227e-04f,  7.2856e-04f,  7.3907e-04f,
 6.5335e-04f,  7.0702e-04f,  7.1233e-04f,
 6.1540e-04f,  6.7286e-04f,  6.7797e-04f,
-3.1496e-02f,  6.0514e-02f,  4.2013e-02f,
-2.8617e-02f,  1.4846e-02f,  4.0016e-03f,
 4.7006e-03f, -4.0017e-02f, -3.0411e-02f,
-9.6037e-03f,  8.8522e-02f,  9.8616e-02f,
 4.1297e-02f, -3.2645e-01f, -7.6144e-03f,
-1.0711e-02f,  3.9324e-02f,  4.0144e-02f,
 5.2899e-02f, -7.8668e-02f, -5.4798e-02f,
-2.0428e-01f,  5.7238e-02f, -3.6937e-02f,
-3.6103e-02f, -8.2683e-02f, -2.8101e-02f,
 8.2479e-02f,  5.7766e-02f, -1.2019e-01f,
-3.8373e-01f,  6.8272e-02f, -1.1758e-02f,
 5.1129e-02f, -2.7931e-01f,  4.5608e-02f,
-2.5151e-02f, -5.0816e-02f,  1.7231e-02f,
-3.6376e-02f,  1.5916e-01f,  2.9192e-02f,
-4.1947e-02f,  5.3183e-02f, -9.7289e-02f,
 4.6138e-02f,  7.0842e-02f,  1.6673e-02f,
-1.7243e-03f,  2.7203e-01f,  3.8262e-02f,
-1.4000e-01f, -7.3793e-02f, -2.0050e-02f,
-1.8750e-02f, -8.5319e-02f, -3.0858e-02f,
-5.9981e-02f,  1.2729e-01f,  1.4094e-02f,
-5.4088e-02f, -2.3694e-02f, -9.7485e-03f,
-4.7840e-03f, -4.8359e-03f, -4.8727e-03f,
-4.7882e-03f, -4.8380e-03f, -4.8755e-03f,
-4.7859e-03f, -4.8321e-03f, -4.8633e-03f,
 4.9511e-02f,  1.0935e-01f, -3.7430e-03f,
 1.1834e-01f,  7.7243e-02f,  4.3074e-02f,
 6.7446e-02f,  2.9734e-02f, -1.1276e-02f,
-2.0080e-02f,  1.3561e-01f, -1.3455e-01f,
-1.4505e-02f,  2.2100e-01f,  4.9635e-02f,
-1.0040e-02f,  3.4560e-02f, -7.4607e-03f,
-6.8873e-02f, -5.6221e-02f,  1.2255e-02f,
-2.9198e-02f,  7.1612e-02f,  2.9402e-02f,
 4.1036e-02f,  4.6417e-02f,  6.0284e-03f,
-6.5261e-02f,  2.1426e-03f,  2.4192e-02f,
-1.6073e-03f, -6.2222e-03f, -1.8295e-02f,
 2.4952e-04f, -2.0623e-02f, -3.3064e-03f,
 5.9188e-02f, -4.8839e-02f,  7.9840e-02f,
-6.7952e-02f, -4.7191e-01f,  1.5117e-01f,
 1.5668e-01f,  2.4733e-01f,  1.1354e-01f,
 1.7742e-02f, -4.4059e-02f,  9.5374e-03f,
 3.2049e-01f, -1.3779e-01f,  9.6608e-02f,
 8.4580e-02f,  1.4293e-01f,  6.1574e-02f,
 2.8777e-03f,  7.8795e-02f, -5.1902e-02f,
 1.2212e-01f,  1.0321e-01f,  3.2360e-02f,
-9.6617e-02f,  7.8941e-03f, -7.0876e-02f,
 3.5869e-03f,  3.5891e-03f,  3.5923e-03f,
 3.5746e-03f,  3.5840e-03f,  3.5967e-03f,
 3.5785e-03f,  3.5932e-03f,  3.6080e-03f,
 1.5454e-03f,  3.0582e-03f,  4.3737e-02f,
-5.9833e-02f, -1.1247e-01f,  4.4380e-02f,
-1.3206e-01f,  8.2778e-03f,  4.7963e-02f,
-4.3720e-02f, -7.5722e-03f,  2.0510e-01f,
 3.0133e-02f, -4.0506e-01f,  2.7867e-01f,
 5.5586e-02f,  2.8926e-02f,  1.3360e-03f,
 1.9490e-05f,  3.3326e-01f, -7.7241e-02f,
-1.5648e-01f,  1.5195e-01f, -1.3995e-01f,
 8.6519e-02f,  1.0447e-01f, -4.1413e-02f,
-3.8667e-03f,  1.6159e-01f,  1.1627e-01f,
-2.2646e-01f, -3.4758e-02f, -6.7956e-03f,
-3.2689e-01f,  1.9606e-01f, -9.1523e-02f,
 1.1238e-02f,  1.5084e-03f,  4.2113e-02f,
-1.1154e-02f, -3.6596e-01f, -7.2252e-02f,
 6.6621e-02f,  1.0188e-01f,  4.1032e-01f,
 3.5892e-02f, -4.8304e-02f,  6.6142e-03f,
 1.3374e-01f,  2.2720e-01f, -7.1224e-02f,
 6.8952e-02f,  2.0467e-01f,  5.0251e-02f,
-6.2016e-02f,  2.2175e-01f, -1.7764e-02f,
 2.7542e-02f,  1.4905e-01f,  3.6637e-02f,
-7.2231e-02f,  5.0271e-03f, -7.1823e-02f,
 3.5760e-03f,  3.5540e-03f,  3.5692e-03f,
 3.5664e-03f,  3.5490e-03f,  3.5689e-03f,
 3.5671e-03f,  3.5619e-03f,  3.5864e-03f,
 2.7470e-02f, -3.9752e-02f,  4.1063e-02f,
-2.4985e-02f, -1.7969e-01f,  8.2186e-02f,
-5.4251e-02f, -5.9651e-03f,  2.5079e-02f,
-2.1197e-02f,  2.5426e-02f,  1.3585e-01f,
-1.3460e-02f, -1.1377e-01f,  1.2278e-01f,
 3.6533e-02f,  1.2843e-02f,  5.6219e-02f,
 5.8141e-04f,  2.8354e-01f, -6.2016e-02f,
-1.0289e-01f,  1.8724e-01f, -9.9475e-02f,
 5.1193e-02f,  7.5986e-02f, -1.2951e-03f,
-8.2587e-02f,  1.8498e-01f,  1.0891e-01f,
 1.3538e-01f, -4.7728e-01f,  1.0868e-01f,
-8.6415e-02f, -1.7061e-01f,  1.0457e-02f
}
};

__constant float biasL[8][8] = 
{
{
-0.1175f, -0.0258f, -0.0053f, -0.0437f, -0.0563f, -0.1047f, -0.3449f,  0.0568f
}
,
{
 0.0339f, -0.1738f,  0.0061f,  0.1565f, -0.0316f, -0.0016f, -0.0032f, -0.0554f
}
,
{
-0.0508f, -0.0609f,  0.0347f, -0.0802f, -0.0438f,  0.2512f, -0.0491f, -0.0259f
}
,
{
 0.0655f,  0.0255f,  0.0228f, -0.0027f, -0.0155f, -0.0163f, -0.0174f, -0.1095f
}
,
{
 4.9947e-03f,  5.3372e-03f, -4.5286e-09f, -1.3756e-03f,  3.8858e-03f, -4.4197e-02f,  3.3970e-02f,  2.8411e-02f
}
,
{
-0.0396f,  0.0007f,  0.1735f,  0.0109f,  0.1177f,  0.0919f,  0.0567f, -0.0005f
}
,
{
 0.0127f, -0.0688f,  0.1102f, -0.0052f,  0.1602f, -0.0191f, -0.0322f,  0.0311f
}
,
{
 0.0063f, 0.0093f, 0.0729f, 0.3734f, 0.0006f, 0.1915f, 0.3186f, 0.2636f
}
};

__constant float kernelsL10[4 * 8] = 
{
-0.0967f, -0.3094f,
 0.3537f,  0.5705f,
 0.2547f,  0.3360f,
-0.0718f, -0.0700f,
-0.3013f, -0.1602f,
 0.4520f,  0.0495f,
 0.1564f,  0.3773f,
-0.0216f,  0.4367f,
-0.4855f, -0.1972f,
-0.2026f, -0.4390f,
 0.3743f, -0.1156f,
 0.4408f, -0.3123f,
-0.3577f,  0.0753f,
-0.3396f,  0.0336f,
 0.1052f, -0.4180f,
 0.0799f, -0.3587f
};


__kernel void conv1To8(
    __read_only image2d_t srcImg, 
    __write_only image2d_array_t tmpImgOut)
{
    const int x = get_global_id(0), y = get_global_id(1);
    if(x >= get_image_width(srcImg) || y >= get_image_height(srcImg))
        return;
    
    const int2 imgIndex1 = (int2)(0, 0);
    const int2 imgIndex2 = (int2)(1, 0);
    int2 coord = (int2)(x, y);

    float4 tl = read_imagef(srcImg, samplerN, (int2)(x-1, y-1));
    float4 tc = read_imagef(srcImg, samplerN, (int2)(x, y-1));
    float4 tr = read_imagef(srcImg, samplerN, (int2)(x+1, y-1));
    float4 ml = read_imagef(srcImg, samplerN, (int2)(x-1, y));
    float4 mc = read_imagef(srcImg, samplerN, coord);
    float4 mr = read_imagef(srcImg, samplerN, (int2)(x+1, y));
    float4 bl = read_imagef(srcImg, samplerN, (int2)(x-1, y+1));
    float4 bc = read_imagef(srcImg, samplerN, (int2)(x, y+1));
    float4 br = read_imagef(srcImg, samplerN, (int2)(x+1, y+1));

    float4 c1234 = RELU((float4)(
        tl.x * kernelsL1[0*9+0] + tc.x * kernelsL1[0*9+1] + tr.x * kernelsL1[0*9+2] +
        ml.x * kernelsL1[0*9+3] + mc.x * kernelsL1[0*9+4] + mr.x * kernelsL1[0*9+5] +
        bl.x * kernelsL1[0*9+6] + bc.x * kernelsL1[0*9+7] + br.x * kernelsL1[0*9+8] + biasL1[0],

        tl.x * kernelsL1[1*9+0] + tc.x * kernelsL1[1*9+1] + tr.x * kernelsL1[1*9+2] +
        ml.x * kernelsL1[1*9+3] + mc.x * kernelsL1[1*9+4] + mr.x * kernelsL1[1*9+5] +
        bl.x * kernelsL1[1*9+6] + bc.x * kernelsL1[1*9+7] + br.x * kernelsL1[1*9+8] + biasL1[1],

        tl.x * kernelsL1[2*9+0] + tc.x * kernelsL1[2*9+1] + tr.x * kernelsL1[2*9+2] +
        ml.x * kernelsL1[2*9+3] + mc.x * kernelsL1[2*9+4] + mr.x * kernelsL1[2*9+5] +
        bl.x * kernelsL1[2*9+6] + bc.x * kernelsL1[2*9+7] + br.x * kernelsL1[2*9+8] + biasL1[2],

        tl.x * kernelsL1[3*9+0] + tc.x * kernelsL1[3*9+1] + tr.x * kernelsL1[3*9+2] +
        ml.x * kernelsL1[3*9+3] + mc.x * kernelsL1[3*9+4] + mr.x * kernelsL1[3*9+5] +
        bl.x * kernelsL1[3*9+6] + bc.x * kernelsL1[3*9+7] + br.x * kernelsL1[3*9+8] + biasL1[3]
    ));
    float4 c5678 = RELU((float4)(
        tl.x * kernelsL1[4*9+0] + tc.x * kernelsL1[4*9+1] + tr.x * kernelsL1[4*9+2] +
        ml.x * kernelsL1[4*9+3] + mc.x * kernelsL1[4*9+4] + mr.x * kernelsL1[4*9+5] +
        bl.x * kernelsL1[4*9+6] + bc.x * kernelsL1[4*9+7] + br.x * kernelsL1[4*9+8] + biasL1[4],

        tl.x * kernelsL1[5*9+0] + tc.x * kernelsL1[5*9+1] + tr.x * kernelsL1[5*9+2] +
        ml.x * kernelsL1[5*9+3] + mc.x * kernelsL1[5*9+4] + mr.x * kernelsL1[5*9+5] +
        bl.x * kernelsL1[5*9+6] + bc.x * kernelsL1[5*9+7] + br.x * kernelsL1[5*9+8] + biasL1[5],

        tl.x * kernelsL1[6*9+0] + tc.x * kernelsL1[6*9+1] + tr.x * kernelsL1[6*9+2] +
        ml.x * kernelsL1[6*9+3] + mc.x * kernelsL1[6*9+4] + mr.x * kernelsL1[6*9+5] +
        bl.x * kernelsL1[6*9+6] + bc.x * kernelsL1[6*9+7] + br.x * kernelsL1[6*9+8] + biasL1[6],

        tl.x * kernelsL1[7*9+0] + tc.x * kernelsL1[7*9+1] + tr.x * kernelsL1[7*9+2] +
        ml.x * kernelsL1[7*9+3] + mc.x * kernelsL1[7*9+4] + mr.x * kernelsL1[7*9+5] +
        bl.x * kernelsL1[7*9+6] + bc.x * kernelsL1[7*9+7] + br.x * kernelsL1[7*9+8] + biasL1[7]
    ));

    write_imagef(tmpImgOut, (int4)(coord, imgIndex1), c1234);
    write_imagef(tmpImgOut, (int4)(coord, imgIndex2), c5678);
}

__kernel void conv8To8(
    __read_only image2d_array_t tmpImgIn,
    __write_only image2d_array_t tmpImgOut,
    int l)
{
    const int x = get_global_id(0), y = get_global_id(1);
    if(x >= get_image_width(tmpImgIn) || y >= get_image_height(tmpImgIn))
        return;

    const int2 imgIndex1 = (int2)(0, 0);
    const int2 imgIndex2 = (int2)(1, 0);
    int4 coord1 = (int4)(x, y, imgIndex1);
    int4 coord2 = (int4)(x, y, imgIndex2);

    float4 tl1 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y-1, imgIndex1));
    float4 tc1 = read_imagef(tmpImgIn, samplerN, (int4)(x, y-1, imgIndex1));
    float4 tr1 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y-1, imgIndex1));
    float4 ml1 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y, imgIndex1));
    float4 mc1 = read_imagef(tmpImgIn, samplerN, coord1);
    float4 mr1 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y, imgIndex1));
    float4 bl1 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y+1, imgIndex1));
    float4 bc1 = read_imagef(tmpImgIn, samplerN, (int4)(x, y+1, imgIndex1));
    float4 br1 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y+1, imgIndex1));

    float4 tl2 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y-1, imgIndex2));
    float4 tc2 = read_imagef(tmpImgIn, samplerN, (int4)(x, y-1, imgIndex2));
    float4 tr2 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y-1, imgIndex2));
    float4 ml2 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y, imgIndex2));
    float4 mc2 = read_imagef(tmpImgIn, samplerN, coord2);
    float4 mr2 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y, imgIndex2));
    float4 bl2 = read_imagef(tmpImgIn, samplerN, (int4)(x-1, y+1, imgIndex2));
    float4 bc2 = read_imagef(tmpImgIn, samplerN, (int4)(x, y+1, imgIndex2));
    float4 br2 = read_imagef(tmpImgIn, samplerN, (int4)(x+1, y+1, imgIndex2));
    
    float4 c1234 = RELU((float4)(
        tl1.x * kernelsL[l][0*72+0*9+0] + tc1.x * kernelsL[l][0*72+0*9+1] + tr1.x * kernelsL[l][0*72+0*9+2] +
        ml1.x * kernelsL[l][0*72+0*9+3] + mc1.x * kernelsL[l][0*72+0*9+4] + mr1.x * kernelsL[l][0*72+0*9+5] +
        bl1.x * kernelsL[l][0*72+0*9+6] + bc1.x * kernelsL[l][0*72+0*9+7] + br1.x * kernelsL[l][0*72+0*9+8] + 

        tl1.y * kernelsL[l][0*72+1*9+0] + tc1.y * kernelsL[l][0*72+1*9+1] + tr1.y * kernelsL[l][0*72+1*9+2] +
        ml1.y * kernelsL[l][0*72+1*9+3] + mc1.y * kernelsL[l][0*72+1*9+4] + mr1.y * kernelsL[l][0*72+1*9+5] +
        bl1.y * kernelsL[l][0*72+1*9+6] + bc1.y * kernelsL[l][0*72+1*9+7] + br1.y * kernelsL[l][0*72+1*9+8] + 

        tl1.z * kernelsL[l][0*72+2*9+0] + tc1.z * kernelsL[l][0*72+2*9+1] + tr1.z * kernelsL[l][0*72+2*9+2] +
        ml1.z * kernelsL[l][0*72+2*9+3] + mc1.z * kernelsL[l][0*72+2*9+4] + mr1.z * kernelsL[l][0*72+2*9+5] +
        bl1.z * kernelsL[l][0*72+2*9+6] + bc1.z * kernelsL[l][0*72+2*9+7] + br1.z * kernelsL[l][0*72+2*9+8] + 

        tl1.w * kernelsL[l][0*72+3*9+0] + tc1.w * kernelsL[l][0*72+3*9+1] + tr1.w * kernelsL[l][0*72+3*9+2] +
        ml1.w * kernelsL[l][0*72+3*9+3] + mc1.w * kernelsL[l][0*72+3*9+4] + mr1.w * kernelsL[l][0*72+3*9+5] +
        bl1.w * kernelsL[l][0*72+3*9+6] + bc1.w * kernelsL[l][0*72+3*9+7] + br1.w * kernelsL[l][0*72+3*9+8] +

        tl2.x * kernelsL[l][0*72+4*9+0] + tc2.x * kernelsL[l][0*72+4*9+1] + tr2.x * kernelsL[l][0*72+4*9+2] +
        ml2.x * kernelsL[l][0*72+4*9+3] + mc2.x * kernelsL[l][0*72+4*9+4] + mr2.x * kernelsL[l][0*72+4*9+5] +
        bl2.x * kernelsL[l][0*72+4*9+6] + bc2.x * kernelsL[l][0*72+4*9+7] + br2.x * kernelsL[l][0*72+4*9+8] + 

        tl2.y * kernelsL[l][0*72+5*9+0] + tc2.y * kernelsL[l][0*72+5*9+1] + tr2.y * kernelsL[l][0*72+5*9+2] +
        ml2.y * kernelsL[l][0*72+5*9+3] + mc2.y * kernelsL[l][0*72+5*9+4] + mr2.y * kernelsL[l][0*72+5*9+5] +
        bl2.y * kernelsL[l][0*72+5*9+6] + bc2.y * kernelsL[l][0*72+5*9+7] + br2.y * kernelsL[l][0*72+5*9+8] + 

        tl2.z * kernelsL[l][0*72+6*9+0] + tc2.z * kernelsL[l][0*72+6*9+1] + tr2.z * kernelsL[l][0*72+6*9+2] +
        ml2.z * kernelsL[l][0*72+6*9+3] + mc2.z * kernelsL[l][0*72+6*9+4] + mr2.z * kernelsL[l][0*72+6*9+5] +
        bl2.z * kernelsL[l][0*72+6*9+6] + bc2.z * kernelsL[l][0*72+6*9+7] + br2.z * kernelsL[l][0*72+6*9+8] + 

        tl2.w * kernelsL[l][0*72+7*9+0] + tc2.w * kernelsL[l][0*72+7*9+1] + tr2.w * kernelsL[l][0*72+7*9+2] +
        ml2.w * kernelsL[l][0*72+7*9+3] + mc2.w * kernelsL[l][0*72+7*9+4] + mr2.w * kernelsL[l][0*72+7*9+5] +
        bl2.w * kernelsL[l][0*72+7*9+6] + bc2.w * kernelsL[l][0*72+7*9+7] + br2.w * kernelsL[l][0*72+7*9+8] + biasL[l][0]
        ,
        tl1.x * kernelsL[l][1*72+0*9+0] + tc1.x * kernelsL[l][1*72+0*9+1] + tr1.x * kernelsL[l][1*72+0*9+2] +
        ml1.x * kernelsL[l][1*72+0*9+3] + mc1.x * kernelsL[l][1*72+0*9+4] + mr1.x * kernelsL[l][1*72+0*9+5] +
        bl1.x * kernelsL[l][1*72+0*9+6] + bc1.x * kernelsL[l][1*72+0*9+7] + br1.x * kernelsL[l][1*72+0*9+8] + 

        tl1.y * kernelsL[l][1*72+1*9+0] + tc1.y * kernelsL[l][1*72+1*9+1] + tr1.y * kernelsL[l][1*72+1*9+2] +
        ml1.y * kernelsL[l][1*72+1*9+3] + mc1.y * kernelsL[l][1*72+1*9+4] + mr1.y * kernelsL[l][1*72+1*9+5] +
        bl1.y * kernelsL[l][1*72+1*9+6] + bc1.y * kernelsL[l][1*72+1*9+7] + br1.y * kernelsL[l][1*72+1*9+8] + 

        tl1.z * kernelsL[l][1*72+2*9+0] + tc1.z * kernelsL[l][1*72+2*9+1] + tr1.z * kernelsL[l][1*72+2*9+2] +
        ml1.z * kernelsL[l][1*72+2*9+3] + mc1.z * kernelsL[l][1*72+2*9+4] + mr1.z * kernelsL[l][1*72+2*9+5] +
        bl1.z * kernelsL[l][1*72+2*9+6] + bc1.z * kernelsL[l][1*72+2*9+7] + br1.z * kernelsL[l][1*72+2*9+8] + 

        tl1.w * kernelsL[l][1*72+3*9+0] + tc1.w * kernelsL[l][1*72+3*9+1] + tr1.w * kernelsL[l][1*72+3*9+2] +
        ml1.w * kernelsL[l][1*72+3*9+3] + mc1.w * kernelsL[l][1*72+3*9+4] + mr1.w * kernelsL[l][1*72+3*9+5] +
        bl1.w * kernelsL[l][1*72+3*9+6] + bc1.w * kernelsL[l][1*72+3*9+7] + br1.w * kernelsL[l][1*72+3*9+8] +

        tl2.x * kernelsL[l][1*72+4*9+0] + tc2.x * kernelsL[l][1*72+4*9+1] + tr2.x * kernelsL[l][1*72+4*9+2] +
        ml2.x * kernelsL[l][1*72+4*9+3] + mc2.x * kernelsL[l][1*72+4*9+4] + mr2.x * kernelsL[l][1*72+4*9+5] +
        bl2.x * kernelsL[l][1*72+4*9+6] + bc2.x * kernelsL[l][1*72+4*9+7] + br2.x * kernelsL[l][1*72+4*9+8] + 

        tl2.y * kernelsL[l][1*72+5*9+0] + tc2.y * kernelsL[l][1*72+5*9+1] + tr2.y * kernelsL[l][1*72+5*9+2] +
        ml2.y * kernelsL[l][1*72+5*9+3] + mc2.y * kernelsL[l][1*72+5*9+4] + mr2.y * kernelsL[l][1*72+5*9+5] +
        bl2.y * kernelsL[l][1*72+5*9+6] + bc2.y * kernelsL[l][1*72+5*9+7] + br2.y * kernelsL[l][1*72+5*9+8] + 

        tl2.z * kernelsL[l][1*72+6*9+0] + tc2.z * kernelsL[l][1*72+6*9+1] + tr2.z * kernelsL[l][1*72+6*9+2] +
        ml2.z * kernelsL[l][1*72+6*9+3] + mc2.z * kernelsL[l][1*72+6*9+4] + mr2.z * kernelsL[l][1*72+6*9+5] +
        bl2.z * kernelsL[l][1*72+6*9+6] + bc2.z * kernelsL[l][1*72+6*9+7] + br2.z * kernelsL[l][1*72+6*9+8] + 

        tl2.w * kernelsL[l][1*72+7*9+0] + tc2.w * kernelsL[l][1*72+7*9+1] + tr2.w * kernelsL[l][1*72+7*9+2] +
        ml2.w * kernelsL[l][1*72+7*9+3] + mc2.w * kernelsL[l][1*72+7*9+4] + mr2.w * kernelsL[l][1*72+7*9+5] +
        bl2.w * kernelsL[l][1*72+7*9+6] + bc2.w * kernelsL[l][1*72+7*9+7] + br2.w * kernelsL[l][1*72+7*9+8] + biasL[l][1]
        ,
        tl1.x * kernelsL[l][2*72+0*9+0] + tc1.x * kernelsL[l][2*72+0*9+1] + tr1.x * kernelsL[l][2*72+0*9+2] +
        ml1.x * kernelsL[l][2*72+0*9+3] + mc1.x * kernelsL[l][2*72+0*9+4] + mr1.x * kernelsL[l][2*72+0*9+5] +
        bl1.x * kernelsL[l][2*72+0*9+6] + bc1.x * kernelsL[l][2*72+0*9+7] + br1.x * kernelsL[l][2*72+0*9+8] + 

        tl1.y * kernelsL[l][2*72+1*9+0] + tc1.y * kernelsL[l][2*72+1*9+1] + tr1.y * kernelsL[l][2*72+1*9+2] +
        ml1.y * kernelsL[l][2*72+1*9+3] + mc1.y * kernelsL[l][2*72+1*9+4] + mr1.y * kernelsL[l][2*72+1*9+5] +
        bl1.y * kernelsL[l][2*72+1*9+6] + bc1.y * kernelsL[l][2*72+1*9+7] + br1.y * kernelsL[l][2*72+1*9+8] + 

        tl1.z * kernelsL[l][2*72+2*9+0] + tc1.z * kernelsL[l][2*72+2*9+1] + tr1.z * kernelsL[l][2*72+2*9+2] +
        ml1.z * kernelsL[l][2*72+2*9+3] + mc1.z * kernelsL[l][2*72+2*9+4] + mr1.z * kernelsL[l][2*72+2*9+5] +
        bl1.z * kernelsL[l][2*72+2*9+6] + bc1.z * kernelsL[l][2*72+2*9+7] + br1.z * kernelsL[l][2*72+2*9+8] + 

        tl1.w * kernelsL[l][2*72+3*9+0] + tc1.w * kernelsL[l][2*72+3*9+1] + tr1.w * kernelsL[l][2*72+3*9+2] +
        ml1.w * kernelsL[l][2*72+3*9+3] + mc1.w * kernelsL[l][2*72+3*9+4] + mr1.w * kernelsL[l][2*72+3*9+5] +
        bl1.w * kernelsL[l][2*72+3*9+6] + bc1.w * kernelsL[l][2*72+3*9+7] + br1.w * kernelsL[l][2*72+3*9+8] +

        tl2.x * kernelsL[l][2*72+4*9+0] + tc2.x * kernelsL[l][2*72+4*9+1] + tr2.x * kernelsL[l][2*72+4*9+2] +
        ml2.x * kernelsL[l][2*72+4*9+3] + mc2.x * kernelsL[l][2*72+4*9+4] + mr2.x * kernelsL[l][2*72+4*9+5] +
        bl2.x * kernelsL[l][2*72+4*9+6] + bc2.x * kernelsL[l][2*72+4*9+7] + br2.x * kernelsL[l][2*72+4*9+8] + 

        tl2.y * kernelsL[l][2*72+5*9+0] + tc2.y * kernelsL[l][2*72+5*9+1] + tr2.y * kernelsL[l][2*72+5*9+2] +
        ml2.y * kernelsL[l][2*72+5*9+3] + mc2.y * kernelsL[l][2*72+5*9+4] + mr2.y * kernelsL[l][2*72+5*9+5] +
        bl2.y * kernelsL[l][2*72+5*9+6] + bc2.y * kernelsL[l][2*72+5*9+7] + br2.y * kernelsL[l][2*72+5*9+8] + 

        tl2.z * kernelsL[l][2*72+6*9+0] + tc2.z * kernelsL[l][2*72+6*9+1] + tr2.z * kernelsL[l][2*72+6*9+2] +
        ml2.z * kernelsL[l][2*72+6*9+3] + mc2.z * kernelsL[l][2*72+6*9+4] + mr2.z * kernelsL[l][2*72+6*9+5] +
        bl2.z * kernelsL[l][2*72+6*9+6] + bc2.z * kernelsL[l][2*72+6*9+7] + br2.z * kernelsL[l][2*72+6*9+8] + 

        tl2.w * kernelsL[l][2*72+7*9+0] + tc2.w * kernelsL[l][2*72+7*9+1] + tr2.w * kernelsL[l][2*72+7*9+2] +
        ml2.w * kernelsL[l][2*72+7*9+3] + mc2.w * kernelsL[l][2*72+7*9+4] + mr2.w * kernelsL[l][2*72+7*9+5] +
        bl2.w * kernelsL[l][2*72+7*9+6] + bc2.w * kernelsL[l][2*72+7*9+7] + br2.w * kernelsL[l][2*72+7*9+8] + biasL[l][2]
        ,
        tl1.x * kernelsL[l][3*72+0*9+0] + tc1.x * kernelsL[l][3*72+0*9+1] + tr1.x * kernelsL[l][3*72+0*9+2] +
        ml1.x * kernelsL[l][3*72+0*9+3] + mc1.x * kernelsL[l][3*72+0*9+4] + mr1.x * kernelsL[l][3*72+0*9+5] +
        bl1.x * kernelsL[l][3*72+0*9+6] + bc1.x * kernelsL[l][3*72+0*9+7] + br1.x * kernelsL[l][3*72+0*9+8] + 

        tl1.y * kernelsL[l][3*72+1*9+0] + tc1.y * kernelsL[l][3*72+1*9+1] + tr1.y * kernelsL[l][3*72+1*9+2] +
        ml1.y * kernelsL[l][3*72+1*9+3] + mc1.y * kernelsL[l][3*72+1*9+4] + mr1.y * kernelsL[l][3*72+1*9+5] +
        bl1.y * kernelsL[l][3*72+1*9+6] + bc1.y * kernelsL[l][3*72+1*9+7] + br1.y * kernelsL[l][3*72+1*9+8] + 

        tl1.z * kernelsL[l][3*72+2*9+0] + tc1.z * kernelsL[l][3*72+2*9+1] + tr1.z * kernelsL[l][3*72+2*9+2] +
        ml1.z * kernelsL[l][3*72+2*9+3] + mc1.z * kernelsL[l][3*72+2*9+4] + mr1.z * kernelsL[l][3*72+2*9+5] +
        bl1.z * kernelsL[l][3*72+2*9+6] + bc1.z * kernelsL[l][3*72+2*9+7] + br1.z * kernelsL[l][3*72+2*9+8] + 

        tl1.w * kernelsL[l][3*72+3*9+0] + tc1.w * kernelsL[l][3*72+3*9+1] + tr1.w * kernelsL[l][3*72+3*9+2] +
        ml1.w * kernelsL[l][3*72+3*9+3] + mc1.w * kernelsL[l][3*72+3*9+4] + mr1.w * kernelsL[l][3*72+3*9+5] +
        bl1.w * kernelsL[l][3*72+3*9+6] + bc1.w * kernelsL[l][3*72+3*9+7] + br1.w * kernelsL[l][3*72+3*9+8] +

        tl2.x * kernelsL[l][3*72+4*9+0] + tc2.x * kernelsL[l][3*72+4*9+1] + tr2.x * kernelsL[l][3*72+4*9+2] +
        ml2.x * kernelsL[l][3*72+4*9+3] + mc2.x * kernelsL[l][3*72+4*9+4] + mr2.x * kernelsL[l][3*72+4*9+5] +
        bl2.x * kernelsL[l][3*72+4*9+6] + bc2.x * kernelsL[l][3*72+4*9+7] + br2.x * kernelsL[l][3*72+4*9+8] + 

        tl2.y * kernelsL[l][3*72+5*9+0] + tc2.y * kernelsL[l][3*72+5*9+1] + tr2.y * kernelsL[l][3*72+5*9+2] +
        ml2.y * kernelsL[l][3*72+5*9+3] + mc2.y * kernelsL[l][3*72+5*9+4] + mr2.y * kernelsL[l][3*72+5*9+5] +
        bl2.y * kernelsL[l][3*72+5*9+6] + bc2.y * kernelsL[l][3*72+5*9+7] + br2.y * kernelsL[l][3*72+5*9+8] + 

        tl2.z * kernelsL[l][3*72+6*9+0] + tc2.z * kernelsL[l][3*72+6*9+1] + tr2.z * kernelsL[l][3*72+6*9+2] +
        ml2.z * kernelsL[l][3*72+6*9+3] + mc2.z * kernelsL[l][3*72+6*9+4] + mr2.z * kernelsL[l][3*72+6*9+5] +
        bl2.z * kernelsL[l][3*72+6*9+6] + bc2.z * kernelsL[l][3*72+6*9+7] + br2.z * kernelsL[l][3*72+6*9+8] + 

        tl2.w * kernelsL[l][3*72+7*9+0] + tc2.w * kernelsL[l][3*72+7*9+1] + tr2.w * kernelsL[l][3*72+7*9+2] +
        ml2.w * kernelsL[l][3*72+7*9+3] + mc2.w * kernelsL[l][3*72+7*9+4] + mr2.w * kernelsL[l][3*72+7*9+5] +
        bl2.w * kernelsL[l][3*72+7*9+6] + bc2.w * kernelsL[l][3*72+7*9+7] + br2.w * kernelsL[l][3*72+7*9+8] + biasL[l][3]
    ));
    float4 c5678 = RELU((float4)(
        tl1.x * kernelsL[l][4*72+0*9+0] + tc1.x * kernelsL[l][4*72+0*9+1] + tr1.x * kernelsL[l][4*72+0*9+2] +
        ml1.x * kernelsL[l][4*72+0*9+3] + mc1.x * kernelsL[l][4*72+0*9+4] + mr1.x * kernelsL[l][4*72+0*9+5] +
        bl1.x * kernelsL[l][4*72+0*9+6] + bc1.x * kernelsL[l][4*72+0*9+7] + br1.x * kernelsL[l][4*72+0*9+8] + 

        tl1.y * kernelsL[l][4*72+1*9+0] + tc1.y * kernelsL[l][4*72+1*9+1] + tr1.y * kernelsL[l][4*72+1*9+2] +
        ml1.y * kernelsL[l][4*72+1*9+3] + mc1.y * kernelsL[l][4*72+1*9+4] + mr1.y * kernelsL[l][4*72+1*9+5] +
        bl1.y * kernelsL[l][4*72+1*9+6] + bc1.y * kernelsL[l][4*72+1*9+7] + br1.y * kernelsL[l][4*72+1*9+8] + 

        tl1.z * kernelsL[l][4*72+2*9+0] + tc1.z * kernelsL[l][4*72+2*9+1] + tr1.z * kernelsL[l][4*72+2*9+2] +
        ml1.z * kernelsL[l][4*72+2*9+3] + mc1.z * kernelsL[l][4*72+2*9+4] + mr1.z * kernelsL[l][4*72+2*9+5] +
        bl1.z * kernelsL[l][4*72+2*9+6] + bc1.z * kernelsL[l][4*72+2*9+7] + br1.z * kernelsL[l][4*72+2*9+8] + 

        tl1.w * kernelsL[l][4*72+3*9+0] + tc1.w * kernelsL[l][4*72+3*9+1] + tr1.w * kernelsL[l][4*72+3*9+2] +
        ml1.w * kernelsL[l][4*72+3*9+3] + mc1.w * kernelsL[l][4*72+3*9+4] + mr1.w * kernelsL[l][4*72+3*9+5] +
        bl1.w * kernelsL[l][4*72+3*9+6] + bc1.w * kernelsL[l][4*72+3*9+7] + br1.w * kernelsL[l][4*72+3*9+8] +

        tl2.x * kernelsL[l][4*72+4*9+0] + tc2.x * kernelsL[l][4*72+4*9+1] + tr2.x * kernelsL[l][4*72+4*9+2] +
        ml2.x * kernelsL[l][4*72+4*9+3] + mc2.x * kernelsL[l][4*72+4*9+4] + mr2.x * kernelsL[l][4*72+4*9+5] +
        bl2.x * kernelsL[l][4*72+4*9+6] + bc2.x * kernelsL[l][4*72+4*9+7] + br2.x * kernelsL[l][4*72+4*9+8] + 

        tl2.y * kernelsL[l][4*72+5*9+0] + tc2.y * kernelsL[l][4*72+5*9+1] + tr2.y * kernelsL[l][4*72+5*9+2] +
        ml2.y * kernelsL[l][4*72+5*9+3] + mc2.y * kernelsL[l][4*72+5*9+4] + mr2.y * kernelsL[l][4*72+5*9+5] +
        bl2.y * kernelsL[l][4*72+5*9+6] + bc2.y * kernelsL[l][4*72+5*9+7] + br2.y * kernelsL[l][4*72+5*9+8] + 

        tl2.z * kernelsL[l][4*72+6*9+0] + tc2.z * kernelsL[l][4*72+6*9+1] + tr2.z * kernelsL[l][4*72+6*9+2] +
        ml2.z * kernelsL[l][4*72+6*9+3] + mc2.z * kernelsL[l][4*72+6*9+4] + mr2.z * kernelsL[l][4*72+6*9+5] +
        bl2.z * kernelsL[l][4*72+6*9+6] + bc2.z * kernelsL[l][4*72+6*9+7] + br2.z * kernelsL[l][4*72+6*9+8] + 

        tl2.w * kernelsL[l][4*72+7*9+0] + tc2.w * kernelsL[l][4*72+7*9+1] + tr2.w * kernelsL[l][4*72+7*9+2] +
        ml2.w * kernelsL[l][4*72+7*9+3] + mc2.w * kernelsL[l][4*72+7*9+4] + mr2.w * kernelsL[l][4*72+7*9+5] +
        bl2.w * kernelsL[l][4*72+7*9+6] + bc2.w * kernelsL[l][4*72+7*9+7] + br2.w * kernelsL[l][4*72+7*9+8] + biasL[l][4]
        ,
        tl1.x * kernelsL[l][5*72+0*9+0] + tc1.x * kernelsL[l][5*72+0*9+1] + tr1.x * kernelsL[l][5*72+0*9+2] +
        ml1.x * kernelsL[l][5*72+0*9+3] + mc1.x * kernelsL[l][5*72+0*9+4] + mr1.x * kernelsL[l][5*72+0*9+5] +
        bl1.x * kernelsL[l][5*72+0*9+6] + bc1.x * kernelsL[l][5*72+0*9+7] + br1.x * kernelsL[l][5*72+0*9+8] + 

        tl1.y * kernelsL[l][5*72+1*9+0] + tc1.y * kernelsL[l][5*72+1*9+1] + tr1.y * kernelsL[l][5*72+1*9+2] +
        ml1.y * kernelsL[l][5*72+1*9+3] + mc1.y * kernelsL[l][5*72+1*9+4] + mr1.y * kernelsL[l][5*72+1*9+5] +
        bl1.y * kernelsL[l][5*72+1*9+6] + bc1.y * kernelsL[l][5*72+1*9+7] + br1.y * kernelsL[l][5*72+1*9+8] + 

        tl1.z * kernelsL[l][5*72+2*9+0] + tc1.z * kernelsL[l][5*72+2*9+1] + tr1.z * kernelsL[l][5*72+2*9+2] +
        ml1.z * kernelsL[l][5*72+2*9+3] + mc1.z * kernelsL[l][5*72+2*9+4] + mr1.z * kernelsL[l][5*72+2*9+5] +
        bl1.z * kernelsL[l][5*72+2*9+6] + bc1.z * kernelsL[l][5*72+2*9+7] + br1.z * kernelsL[l][5*72+2*9+8] + 

        tl1.w * kernelsL[l][5*72+3*9+0] + tc1.w * kernelsL[l][5*72+3*9+1] + tr1.w * kernelsL[l][5*72+3*9+2] +
        ml1.w * kernelsL[l][5*72+3*9+3] + mc1.w * kernelsL[l][5*72+3*9+4] + mr1.w * kernelsL[l][5*72+3*9+5] +
        bl1.w * kernelsL[l][5*72+3*9+6] + bc1.w * kernelsL[l][5*72+3*9+7] + br1.w * kernelsL[l][5*72+3*9+8] +

        tl2.x * kernelsL[l][5*72+4*9+0] + tc2.x * kernelsL[l][5*72+4*9+1] + tr2.x * kernelsL[l][5*72+4*9+2] +
        ml2.x * kernelsL[l][5*72+4*9+3] + mc2.x * kernelsL[l][5*72+4*9+4] + mr2.x * kernelsL[l][5*72+4*9+5] +
        bl2.x * kernelsL[l][5*72+4*9+6] + bc2.x * kernelsL[l][5*72+4*9+7] + br2.x * kernelsL[l][5*72+4*9+8] + 

        tl2.y * kernelsL[l][5*72+5*9+0] + tc2.y * kernelsL[l][5*72+5*9+1] + tr2.y * kernelsL[l][5*72+5*9+2] +
        ml2.y * kernelsL[l][5*72+5*9+3] + mc2.y * kernelsL[l][5*72+5*9+4] + mr2.y * kernelsL[l][5*72+5*9+5] +
        bl2.y * kernelsL[l][5*72+5*9+6] + bc2.y * kernelsL[l][5*72+5*9+7] + br2.y * kernelsL[l][5*72+5*9+8] + 

        tl2.z * kernelsL[l][5*72+6*9+0] + tc2.z * kernelsL[l][5*72+6*9+1] + tr2.z * kernelsL[l][5*72+6*9+2] +
        ml2.z * kernelsL[l][5*72+6*9+3] + mc2.z * kernelsL[l][5*72+6*9+4] + mr2.z * kernelsL[l][5*72+6*9+5] +
        bl2.z * kernelsL[l][5*72+6*9+6] + bc2.z * kernelsL[l][5*72+6*9+7] + br2.z * kernelsL[l][5*72+6*9+8] + 

        tl2.w * kernelsL[l][5*72+7*9+0] + tc2.w * kernelsL[l][5*72+7*9+1] + tr2.w * kernelsL[l][5*72+7*9+2] +
        ml2.w * kernelsL[l][5*72+7*9+3] + mc2.w * kernelsL[l][5*72+7*9+4] + mr2.w * kernelsL[l][5*72+7*9+5] +
        bl2.w * kernelsL[l][5*72+7*9+6] + bc2.w * kernelsL[l][5*72+7*9+7] + br2.w * kernelsL[l][5*72+7*9+8] + biasL[l][5]
        ,
        tl1.x * kernelsL[l][6*72+0*9+0] + tc1.x * kernelsL[l][6*72+0*9+1] + tr1.x * kernelsL[l][6*72+0*9+2] +
        ml1.x * kernelsL[l][6*72+0*9+3] + mc1.x * kernelsL[l][6*72+0*9+4] + mr1.x * kernelsL[l][6*72+0*9+5] +
        bl1.x * kernelsL[l][6*72+0*9+6] + bc1.x * kernelsL[l][6*72+0*9+7] + br1.x * kernelsL[l][6*72+0*9+8] + 

        tl1.y * kernelsL[l][6*72+1*9+0] + tc1.y * kernelsL[l][6*72+1*9+1] + tr1.y * kernelsL[l][6*72+1*9+2] +
        ml1.y * kernelsL[l][6*72+1*9+3] + mc1.y * kernelsL[l][6*72+1*9+4] + mr1.y * kernelsL[l][6*72+1*9+5] +
        bl1.y * kernelsL[l][6*72+1*9+6] + bc1.y * kernelsL[l][6*72+1*9+7] + br1.y * kernelsL[l][6*72+1*9+8] + 

        tl1.z * kernelsL[l][6*72+2*9+0] + tc1.z * kernelsL[l][6*72+2*9+1] + tr1.z * kernelsL[l][6*72+2*9+2] +
        ml1.z * kernelsL[l][6*72+2*9+3] + mc1.z * kernelsL[l][6*72+2*9+4] + mr1.z * kernelsL[l][6*72+2*9+5] +
        bl1.z * kernelsL[l][6*72+2*9+6] + bc1.z * kernelsL[l][6*72+2*9+7] + br1.z * kernelsL[l][6*72+2*9+8] + 

        tl1.w * kernelsL[l][6*72+3*9+0] + tc1.w * kernelsL[l][6*72+3*9+1] + tr1.w * kernelsL[l][6*72+3*9+2] +
        ml1.w * kernelsL[l][6*72+3*9+3] + mc1.w * kernelsL[l][6*72+3*9+4] + mr1.w * kernelsL[l][6*72+3*9+5] +
        bl1.w * kernelsL[l][6*72+3*9+6] + bc1.w * kernelsL[l][6*72+3*9+7] + br1.w * kernelsL[l][6*72+3*9+8] +

        tl2.x * kernelsL[l][6*72+4*9+0] + tc2.x * kernelsL[l][6*72+4*9+1] + tr2.x * kernelsL[l][6*72+4*9+2] +
        ml2.x * kernelsL[l][6*72+4*9+3] + mc2.x * kernelsL[l][6*72+4*9+4] + mr2.x * kernelsL[l][6*72+4*9+5] +
        bl2.x * kernelsL[l][6*72+4*9+6] + bc2.x * kernelsL[l][6*72+4*9+7] + br2.x * kernelsL[l][6*72+4*9+8] + 

        tl2.y * kernelsL[l][6*72+5*9+0] + tc2.y * kernelsL[l][6*72+5*9+1] + tr2.y * kernelsL[l][6*72+5*9+2] +
        ml2.y * kernelsL[l][6*72+5*9+3] + mc2.y * kernelsL[l][6*72+5*9+4] + mr2.y * kernelsL[l][6*72+5*9+5] +
        bl2.y * kernelsL[l][6*72+5*9+6] + bc2.y * kernelsL[l][6*72+5*9+7] + br2.y * kernelsL[l][6*72+5*9+8] + 

        tl2.z * kernelsL[l][6*72+6*9+0] + tc2.z * kernelsL[l][6*72+6*9+1] + tr2.z * kernelsL[l][6*72+6*9+2] +
        ml2.z * kernelsL[l][6*72+6*9+3] + mc2.z * kernelsL[l][6*72+6*9+4] + mr2.z * kernelsL[l][6*72+6*9+5] +
        bl2.z * kernelsL[l][6*72+6*9+6] + bc2.z * kernelsL[l][6*72+6*9+7] + br2.z * kernelsL[l][6*72+6*9+8] + 

        tl2.w * kernelsL[l][6*72+7*9+0] + tc2.w * kernelsL[l][6*72+7*9+1] + tr2.w * kernelsL[l][6*72+7*9+2] +
        ml2.w * kernelsL[l][6*72+7*9+3] + mc2.w * kernelsL[l][6*72+7*9+4] + mr2.w * kernelsL[l][6*72+7*9+5] +
        bl2.w * kernelsL[l][6*72+7*9+6] + bc2.w * kernelsL[l][6*72+7*9+7] + br2.w * kernelsL[l][6*72+7*9+8] + biasL[l][6]
        ,
        tl1.x * kernelsL[l][7*72+0*9+0] + tc1.x * kernelsL[l][7*72+0*9+1] + tr1.x * kernelsL[l][7*72+0*9+2] +
        ml1.x * kernelsL[l][7*72+0*9+3] + mc1.x * kernelsL[l][7*72+0*9+4] + mr1.x * kernelsL[l][7*72+0*9+5] +
        bl1.x * kernelsL[l][7*72+0*9+6] + bc1.x * kernelsL[l][7*72+0*9+7] + br1.x * kernelsL[l][7*72+0*9+8] + 

        tl1.y * kernelsL[l][7*72+1*9+0] + tc1.y * kernelsL[l][7*72+1*9+1] + tr1.y * kernelsL[l][7*72+1*9+2] +
        ml1.y * kernelsL[l][7*72+1*9+3] + mc1.y * kernelsL[l][7*72+1*9+4] + mr1.y * kernelsL[l][7*72+1*9+5] +
        bl1.y * kernelsL[l][7*72+1*9+6] + bc1.y * kernelsL[l][7*72+1*9+7] + br1.y * kernelsL[l][7*72+1*9+8] + 

        tl1.z * kernelsL[l][7*72+2*9+0] + tc1.z * kernelsL[l][7*72+2*9+1] + tr1.z * kernelsL[l][7*72+2*9+2] +
        ml1.z * kernelsL[l][7*72+2*9+3] + mc1.z * kernelsL[l][7*72+2*9+4] + mr1.z * kernelsL[l][7*72+2*9+5] +
        bl1.z * kernelsL[l][7*72+2*9+6] + bc1.z * kernelsL[l][7*72+2*9+7] + br1.z * kernelsL[l][7*72+2*9+8] + 

        tl1.w * kernelsL[l][7*72+3*9+0] + tc1.w * kernelsL[l][7*72+3*9+1] + tr1.w * kernelsL[l][7*72+3*9+2] +
        ml1.w * kernelsL[l][7*72+3*9+3] + mc1.w * kernelsL[l][7*72+3*9+4] + mr1.w * kernelsL[l][7*72+3*9+5] +
        bl1.w * kernelsL[l][7*72+3*9+6] + bc1.w * kernelsL[l][7*72+3*9+7] + br1.w * kernelsL[l][7*72+3*9+8] +

        tl2.x * kernelsL[l][7*72+4*9+0] + tc2.x * kernelsL[l][7*72+4*9+1] + tr2.x * kernelsL[l][7*72+4*9+2] +
        ml2.x * kernelsL[l][7*72+4*9+3] + mc2.x * kernelsL[l][7*72+4*9+4] + mr2.x * kernelsL[l][7*72+4*9+5] +
        bl2.x * kernelsL[l][7*72+4*9+6] + bc2.x * kernelsL[l][7*72+4*9+7] + br2.x * kernelsL[l][7*72+4*9+8] + 

        tl2.y * kernelsL[l][7*72+5*9+0] + tc2.y * kernelsL[l][7*72+5*9+1] + tr2.y * kernelsL[l][7*72+5*9+2] +
        ml2.y * kernelsL[l][7*72+5*9+3] + mc2.y * kernelsL[l][7*72+5*9+4] + mr2.y * kernelsL[l][7*72+5*9+5] +
        bl2.y * kernelsL[l][7*72+5*9+6] + bc2.y * kernelsL[l][7*72+5*9+7] + br2.y * kernelsL[l][7*72+5*9+8] + 

        tl2.z * kernelsL[l][7*72+6*9+0] + tc2.z * kernelsL[l][7*72+6*9+1] + tr2.z * kernelsL[l][7*72+6*9+2] +
        ml2.z * kernelsL[l][7*72+6*9+3] + mc2.z * kernelsL[l][7*72+6*9+4] + mr2.z * kernelsL[l][7*72+6*9+5] +
        bl2.z * kernelsL[l][7*72+6*9+6] + bc2.z * kernelsL[l][7*72+6*9+7] + br2.z * kernelsL[l][7*72+6*9+8] + 

        tl2.w * kernelsL[l][7*72+7*9+0] + tc2.w * kernelsL[l][7*72+7*9+1] + tr2.w * kernelsL[l][7*72+7*9+2] +
        ml2.w * kernelsL[l][7*72+7*9+3] + mc2.w * kernelsL[l][7*72+7*9+4] + mr2.w * kernelsL[l][7*72+7*9+5] +
        bl2.w * kernelsL[l][7*72+7*9+6] + bc2.w * kernelsL[l][7*72+7*9+7] + br2.w * kernelsL[l][7*72+7*9+8] + biasL[l][7]
    ));

    write_imagef(tmpImgOut, coord1, c1234);
    write_imagef(tmpImgOut, coord2, c5678);
}

__kernel void convTranspose8To1(
    __read_only image2d_array_t tmpImgIn,
    __write_only image2d_t dstImg)
{
    const int x = get_global_id(0), y = get_global_id(1);
    if(x >= get_image_width(dstImg) || y >= get_image_height(dstImg))
        return;

    int2 coord = (int2)(x, y);
    int2 orgCoord = coord / 2;

    int2 pos = coord & 1;
    int index = pos.y * 2 + pos.x;

    float4 mc1 = read_imagef(tmpImgIn, samplerN, (int4)(orgCoord, 0, 0));
    float4 mc2 = read_imagef(tmpImgIn, samplerN, (int4)(orgCoord, 1, 0));

    float c = clamp(
        mc1.x * kernelsL10[0 + index] +
        mc1.y * kernelsL10[4 + index] +
        mc1.z * kernelsL10[8 + index] +
        mc1.w * kernelsL10[12 + index] +
        mc2.x * kernelsL10[16 + index] +
        mc2.y * kernelsL10[20 + index] +
        mc2.z * kernelsL10[24 + index] +
        mc2.w * kernelsL10[28 + index], 0.0f, 1.0f);

    write_imagef(dstImg, coord, (float4)(c, 0.0f, 0.0f, 1.0f));
}
